1. Probabilistic Situation Assessment for Intelligent Vehicles with Uncertain Trajectory Distribution
- Author
-
Heye Huang, Jinxin Liu, Xunjia Zheng, Jianqiang Wang, and Wenjun Liu
- Subjects
050210 logistics & transportation ,0209 industrial biotechnology ,Mathematical optimization ,Distribution (number theory) ,Computer science ,Mechanical Engineering ,05 social sciences ,Probabilistic logic ,02 engineering and technology ,020901 industrial engineering & automation ,0502 economics and business ,Trajectory ,Civil and Structural Engineering ,Situation analysis - Abstract
Situation assessment is crucial for intelligent vehicles, enabling detection of potential risks to dynamic and complex traffic environments. In this paper, we propose a unified framework that tackles the coupling relationships between traffic participants and quantifies the possible range of vehicle trajectory generation and the expected losses caused by risk source attributes in the driving process. We first apply the state space trajectory planning scheme based on a sampling algorithm to generate the path candidates; each feasible path is designed through a parametric cubic spline. Then, to evaluate the risk range in the driving process, we quantify the interaction of traffic participants, and employ the principle of least action to calculate the cost of each feasible path when achieving the destination. The probability distribution map, namely the possible range of driving trajectories, can be obtained based on the path cost. Furthermore, the vehicle-to-vehicle interaction is calculated based on the equivalent force, which estimates the expected accident losses. Finally, the vehicle trajectory prediction and the expected loss are combined to output the probabilistic situation assessment of intelligent vehicles. The algorithm is implemented in different scenarios and applied to the trajectory planning process. Results demonstrate that, compared with the classical situation assessment metric, the developed method can determine and accurately identify the influence range of driving risk in real-time, predict a dangerous situation earlier, and ensure the vehicle avoids obstacles in advance.
- Published
- 2021
- Full Text
- View/download PDF